# Binaryjaccardindex

> Compute the BinaryJaccardIndex metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute BinaryJaccardIndex, or asks how to score with BinaryJaccardIndex.

- Skill: `qhjqhj00/binaryjaccardindex` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/binaryjaccardindex`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/binaryjaccardindex/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/binaryjaccardindex

---


# binaryjaccardindex

> Metric `BinaryJaccardIndex` from `torchmetrics` (torchmetrics.classification.BinaryJaccardIndex)

## When to invoke this skill

The user has predictions + ground truth and asks to evaluate with BinaryJaccardIndex, or
mentions `torchmetrics.classification.BinaryJaccardIndex` directly, or wants the standard torchmetrics implementation.

## Reference signature

```python
from torchmetrics.classification import BinaryJaccardIndex

# BinaryJaccardIndex(threshold: float = 0.5, ignore_index: Optional[int] = None, validate_args: bool = True, zero_division: float = 0, **kwargs: Any) -> None
```

## Library docstring

```
Calculate the Jaccard index for binary tasks.

The `Jaccard index`_ (also known as the intersection over union or jaccard similarity coefficient) is an statistic
that can be used to determine the similarity and diversity of a sample set. It is defined as the size of the
intersection divided by the union of the sample sets:

.. math:: J(A,B) = \frac{|A\cap B|}{|A\cup B|}

As input to ``forward`` and ``update`` the metric accepts the following input:

- ``preds`` (:class:`~torch.Tensor`): A int or float tensor of shape ``(N, ...)``. If preds is a floating point
  tensor with values outside [0,1] range we consider the input to be logits and will auto apply sigmoid per element.
  Additionally, we convert to int tensor with thresholding using the value in ``threshold``.
- ``target`` (:class:`~torch.Tensor`): An int tensor of shape ``(N, ...)``.

.. tip::
   Additional dimension ``...`` will be flattened into the batch dimension.

As output to ``forward`` and ``compute`` the metric returns the following output:

- ``bji`` (:class:`~torch.Tensor`): A tensor containing the Binary Jaccard Index.

Args:
    threshold: Threshold for transforming probability to binary (0,1) predictions
    ignore_index:
        Specifies a target value that is ignored and does not contribute to the metric calculation
    validate_args: bool indicating if input arguments and tensors should be validated for correctness.
        Set to ``False`` for faster computations.
    zero_division:
        Value to replace when there is a division by zero. Should be `0` or `1`.
    kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

Example (preds is int tensor):
    >>> from torch import tensor
    >>> from torchmetrics.classification import BinaryJaccardIndex
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0, 1, 0, 0])
    >>> metric = BinaryJaccardIndex()
    >>> metric(preds, target)
    tensor(0.5000)

Example (preds is float tensor):
    >>> from torchmetrics.classification import BinaryJaccardIndex
    >>> target = tensor([1, 1, 0, 0])
    >>> preds = tensor([0.35, 0.85, 0.48, 0.01])
    >>> metric = BinaryJaccardIndex()
    >>> metric(preds, target)
    t
```

## Quick recipe

```python
import torchmetrics.classification as _m
score = _m.BinaryJaccardIndex(y_true, y_pred)
```

## Don'ts

- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.